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Abstract:
We present a Hidden Markov Model (HMM) for infering the
hidden psychological state (or neural activity) during single trial
fMRI activation experiments with blocked task paradigms. Inference
is based on Bayesian methodology, using a combination of analytical
and a variety of Markov Chain Monte Carlo (MCMC) sampling
techniques. The advantage of this method is that detection of short
time learning effects between repeated trials is possible since
inference is based only on single trial experiments
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